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Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

Hussain, Yawar, Satgé, Frédéric, Hussain, MuhammadBabar, Martinez-Carvajal, Hernan, Bonnet, Marie-Paule, Cárdenas-Soto, Martin, Roig, HenriqueLlacer, Akhter, Gulraiz
Theoretical and applied climatology 2018 v.131 no.3-4 pp. 1119-1132
absorption, arid zones, data collection, glaciers, ice, mountains, rain, satellites, snow, surface temperature, Pakistan
The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.